August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Continuous tracking as a general tool to study the dynamics and context effects of human perception
Author Affiliations
  • David Burr
    University of Florence, Florence, Italy
  • Pierfrancesco Ambrosi
    University of Florence, Florence, Italy
  • Guido Marco Cicchini
    National Research Council, Pisa, Italy
Journal of Vision August 2023, Vol.23, 4635. doi:https://doi.org/10.1167/jov.23.9.4635
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      David Burr, Pierfrancesco Ambrosi, Guido Marco Cicchini; Continuous tracking as a general tool to study the dynamics and context effects of human perception. Journal of Vision 2023;23(9):4635. https://doi.org/10.1167/jov.23.9.4635.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Continuous tracking is a newly developed technique that measures the correlation between a randomly changing stimulus property (usually 2-D position) and the response of participants tracking the object. This technique can in principle by generalised to measure any dynamic aspect of a stimulus, to provide useful information not only about sensitivity, but also dynamics and contextual effects. Here we apply it to motion and numerosity. Participants tracked the direction of motion of 1-D noise moving randomly over a randomly moving background, target and background following independent motion trajectories. Observer responses correlated positively with the target motion, and negatively with the background motion, demonstrating and quantifying surround inhibition of motion. Separately, participants tracked on a number-line the perceived numerosity of a cloud of dots. Some dot-pairs were connected by lines, producing an illusory reduction of the apparent numerosity of the dot clouds: both the number of dots and the proportion connected by lines varied over time, following independent random walks. The tracking correlations showed that grouping dots by connecting lines caused a robust underestimation of numerosity. The tracking response to the illusion created by connection was about 150 ms slower than to the physical numerosity, suggesting that this time was utilised in processing the grouping effect. Finally, we developed an ideal observer model that closely models human results, providing a generalized framework for modelling the effects on tracking data, and to study the divergence of human participants from ideal behavior.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×